“One of the biggest problems in science today is moving forward and finding the underlying principles in areas where there’s lots and lots of data but where there are theoretical gaps,” said Hod Lipson,
a professor of computer and information science at Cornell and an author of the Science paper. “I think this is going to be an important tool.”

In the same issue of Science, scientists in Britain describe building a robot that can not only devise a hypothesis but can also run and analyze experiments to test the hypothesis.

The Cornell computer program uses a technique called genetic programming that starts with random guesses at a solution and then employs an evolution-inspired algorithm to shuffle and change pieces of the equations until it
finds a solution that works.

“We do not use any expert knowledge to generate any a priori model,” Dr. Lipson said. “We just start with some building blocks.”

In the past, genetic programming has been used to generate models to describe phenomena like the flow of fluids or the gyrations of stock prices.

The twist of the Cornell research is that Dr. Lipson and Michael Schmidt, a graduate student, used the technique to look for combinations of the experimental variables that remained constant even as the variables changed over
time. Such invariant equations often point to fundamental natural laws.

Thus, instead of an equation describing, for example, the back-and-forth swaying of a pendulum, the computer discovered principles like the conservation of energy and momentum. “You get something that is deeper about
the data,” Dr. Lipson said.

The system successfully found such physical laws within experimental data taken from complex, chaotic systems like a double pendulum — a pendulum with a pivot joint in the middle.

“If you just look at the data plainly, it’s difficult to see if there’s anything systematic going on there,” Dr. Lipson said. “But despite that fact, when the algorithm analyzed that data,
it could see laws that we know are correct.”

When the scientists fed the computer random numbers, the computer correctly found nothing.

“It’s a nice piece of work,” said John R. Koza, a computer scientist at Stanford who pioneered genetic programming. “It’s another good example of how genetic programming can do things that
are comparable to what human scientists can do.”

The scientists in Britain, led by Ross D. King, a professor at Aberystwyth University, built a robotic scientist they named Adam.
Using artificial intelligence, it came up with a hypothesis about genes in baker’s yeast and the enzymes produced by the genes. It then designed and ran experiments to test its hypothesis. Using the results, it revised
its hypothesis and ran more experiments before arriving at its conclusions.

Human scientists then repeated the experiments. Adam was right.

“It’s actually showed you can make the system sophisticated enough to discover novel scientific knowledge,” Dr. King said.

A follow-up robot is named Eve, which will search for drugs to combat diseases like malaria.

Neither Dr. Lipson nor Dr. King thinks scientists will be put out of work any time soon. “It’s helping scientists and making science more efficient,” Dr. King said.

Robotic descendants of Adam might be able to conduct many of the numbingly repetitive experiments now performed by graduate students, giving them more time to think about the actual science, he said.

Dr. Lipson and Mr. Schmidt have shifted their attention from classical physics, which Newton solved centuries ago, to biology, where many mysteries remain.

The scientists applied the genetic programming algorithm to previously published data from metabolic reaction experiments, and the computer program dutifully churned out some perplexing laws concerning the chemical concentrations
in the reactions.

That leads to a new conundrum. Like Johnny Carson’s old “Carnac
the Magnificent” character, the scientists have the answer, but do not know the question.

“It’s as if you go to an oracle and you ask, ‘Tell me what’s going on here,’ and you get this equation, but then it doesn’t come with any explanation,” Dr. Lipson said of the
new laws the computer discovered. “We’re pretty sure they’re correct. But we don’t know where they’re coming from. We don’t know what they’re explaining.”

And that should give the scientists some job security.

A version of this article appeared in print on April 7, 2009, on page D4 of the New York edition.